Segmentation of nodules on chest computed tomography for growth assessment

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a real...

Full description

Saved in:
Bibliographic Details
Published in:Medical physics (Lancaster) Vol. 31; no. 4; pp. 839 - 848
Main Authors: Mullally, William, Betke, Margrit, Wang, Jingbin, Ko, Jane P.
Format: Journal Article
Language:English
Published: United States American Association of Physicists in Medicine 01.04.2004
Subjects:
3D algorithms
Algorithms
Artificial Intelligence
cancer
Computed radiography
computed tomography
computer aided analysis
computerised tomography
computer‐aided diagnosis
Humans
image segmentation
Imaging, Three-Dimensional
lung
lung cancer
medical image processing
Neoplasm Staging - methods
Pattern Recognition, Automated
phantom study
phantoms
Phantoms, Imaging
Physicists
Radiographic Image Interpretation, Computer-Assisted - instrumentation
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography, Thoracic - instrumentation
Radiography, Thoracic - methods
Radiologists
Reproducibility of Results
Sensitivity and Specificity
shape analysis
Solitary Pulmonary Nodule - classification
Solitary Pulmonary Nodule - diagnostic imaging
Solitary Pulmonary Nodule - pathology
tumours
volume effects
lung cancer
phantom study
computed tomography
volume effects
3D algorithms
image segmentation
shape analysis
computer-aided diagnosis
ISSN:0094-2405, 2473-4209
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods’ estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Undefined-3
ISSN:0094-2405
2473-4209
DOI:10.1118/1.1656593